Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Coco, Moreno I.; Mønster, Dan; Leonardi, Giuseppe; Dale, Rick; Wallot, Sebastian

doi:10.48550/arxiv.2006.01954

Cited by 4 publications

(4 citation statements)

References 50 publications

(74 reference statements)

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“…In the analysis of marine time-series data, we used CRQA to quantify the frequency of similar changes between the time series of two water quality factors and then confirmed the dynamic spatial correlation of the time series of different water quality factors. CRQA indexes include mean diagonal line length (MDL), determinism (DET), laminarity (LAM), and recurrence rate (RR) [ 14 , 15 , 16 , 17 , 18 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Sun

Wang

et al. 2023

Entropy

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In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

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Section: Materials and Methodsmentioning

confidence: 99%

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Sun

Wang

et al. 2023

Entropy

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“…RQA is a versatile method that makes few assumptions and is robust in the face of outlying data points and non-stationarity (Webber & Zbilut, 2005), making it an attractive method to apply to biological signals, such as pupil data. RQA can be used to ask different questions, such as how predictable and stable a time series is, or whether and when qualitative or quantitative changes occur in a time series (Coco et al, 2020). As the name implies, recurrence -that things repeat themselves -is the central concept of RQA.…”

Section: Recurrence-based Analysesmentioning

confidence: 99%

From pre-processing to advanced dynamic modeling of pupil data

et al. 2023

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The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants’ pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

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“…In order to generate RR, DET, Entr and LAM data, which are chaos indicators, Coco et al (2020) prepared by (CRQA) software was used. Although the CRQA software package was originally prepared for Cross RQA structures, instead of choosing different Figure 1 €/$ Daily Exchange Rate, Source: Yahoo variables, it turns into an RQA structure if both variables are the same.…”

Section: The Data Set Preparationmentioning

confidence: 99%

Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method

YALÇINKAYA

Başaran

2023

Chaos Theory and Applications

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A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatility information to affect each other, and which information affects and which information is affected. It is very important to determine the causes of volatility, which is an important result indicator for the finance literature, and especially with this study, it was tried to determine whether the chaos data is in a causal relationship with volatility.If some of the chaos data can be identified as the cause of volatility, the detected chaos data can be used in other research as a leading indicator of volatility. The data set used in the study is the daily €/$ exchange rate index between 01.01.2005 and 10.11.2022. In the study, time series of chaos data were created with Windowed RQA method and Hatemi-J asymmetric causality analysis research was carried out between these time series and €/$ exchange rate index volatility. The findings of the study conclude that the chaos data LnRR, LnEntr and LnLAM could be used as leading indicators of the €/$ exchange rate index volatility.

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Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Cited by 4 publications

References 50 publications

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

From pre-processing to advanced dynamic modeling of pupil data

Investigation of the Relationship Between Chaos Data and €/$ Exchange Rate Index Data with RQA Method

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